by Air Force Human Resources Laboratory, Air Force Systems Command in Brooks Air Force Base, Tex .
Written in English
|Statement||William B. Rouse, William B. Johnson.|
|Series||AFHRL technical paper -- 89-78, AFHRL-technical paper -- 89-78.|
|Contributions||Johnson, William B., Air Force Human Resources Laboratory.|
|The Physical Object|
Performance aiding and training have been viewed historically as distinct approaches to human performance enhancement. But should they be treated as distinct? In this chapter, I argue that these approaches are complementary and, in fact, can be viewed as lying on a continuum with aiding at one end and training at the other : Azad M. Madni. These approaches are evaluated in terms of their advantages and disadvantages when used to analyze training/aiding tradeoffs. These evaluations lead to the synthesis of three composite approaches. Exploring the Tradeoffs between Programmability and Efﬁciency in Data-Parallel Accelerators Fig. 1. Different types of data-level parallelism. Examples are expressed in a C-like pseudocode and are ordered from regular DLP (regular data access (DA) and control ﬂow (CF)) to irregular DLP (irregular data access (DA) and control ﬂow (CF)). These approaches are evaluated in terms of their advantages and disadvantages when used to analyze training/aiding tradeoffs. These evaluations lead to the synthesis of three.
Computational Trade-offs in Statistical Learning Alekh Agarwal vast amounts of training data for learning algorithms are often readily available. The goal of this thesis is to study these trade-oﬀs between the computational and statis-tical aspects of learning problems. This line of research results in several natural questions,Cited by: 4. Abstract: The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful. We propose a framework that provides an explicit opportunity for practitioners to specify how much statistical risk they are willing to accept for a given computational cost, and Cited by: 1. Several other themes and approaches appear in work among researchers exploring computational tradeoffs. A number of investigators have explored time–space tradeoffs, alluding to analogous results developed in the Computational Theory community on the relationship between time and space in algorithmic complexity. Computational and Statistical Tradeoffs in Learning to Rank Ashish Khetan and Sewoong Oh Department of ISE, University of Illinois at Urbana-Champaign Email: fkhetan2,swoh [email protected] Abstract For massive and heterogeneous modern data sets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational.
There are many approaches to completing a formal analysis of tradeoffs. This post will summarize two. Important decisions include multiple, sometimes competing factors. A tradeoff is the giving up of one thing in return for another. Consider the difference between an uptime of percent and an uptime of percent. An uptime of percent means the network is down 30 minutes per week, which is not acceptable to many customers. An uptime of percent means the network is down 5 minutes per week, which might be acceptable, depending on the type of business. 9File Size: KB. to all others. Instead, di erent approaches have unique advantages and disadvantages. To set the stage for our own work, we will brie y compare dictionary, unsupervised learning, and supervised learning approaches. Dictionary or keyword based approaches take an axiomatic approach to clas-si cation. Statistical and Computational Tradeoffs “Computational limitations of statistical problems have largely been ignored or simply overcome by ad hoc relaxations techniques. If optimal methods cannot be computed in reasonable time, what is the best possible statistical performance of a computationally efficient procedure?